import pandas as pd
import numpy as np
import igraph as ig
import collections
import matplotlib
%matplotlib inline

Explore Data

df_unweighted = pd.read_csv('combined_final_unweighted.csv')
df_unweighted

	node_start	node_end	status
0	Hong Kong	Samoa	jurisdiction
1	Hong Kong	Samoa	jurisdiction
2	Hong Kong	Samoa	jurisdiction
3	Hong Kong	Samoa	jurisdiction
4	Hong Kong	Samoa	jurisdiction
...	...	...	...
338127	Indonesia	British Virgin Islands	officer_base
338128	Cook Islands	British Virgin Islands	officer_base
338129	Hong Kong	British Virgin Islands	officer_base
338130	Cook Islands	United Kingdom	officer_base
338131	Hong Kong	United Kingdom	officer_base

338132 rows × 3 columns

df_weighted = pd.read_csv('combined_final_weighted.csv')
df_weighted

	node_start	node_end	status	weight
0	Hong Kong	Samoa	jurisdiction	2.0
1	Hong Kong	Samoa	jurisdiction	2.0
2	Hong Kong	Samoa	jurisdiction	2.0
3	Hong Kong	Samoa	jurisdiction	2.0
4	Hong Kong	Samoa	jurisdiction	2.0
...	...	...	...	...
338119	Indonesia	British Virgin Islands	officer_base	1.0
338120	Cook Islands	British Virgin Islands	officer_base	1.0
338121	Hong Kong	British Virgin Islands	officer_base	1.0
338122	Cook Islands	United Kingdom	officer_base	1.0
338123	Hong Kong	United Kingdom	officer_base	1.0

338124 rows × 4 columns

df_weighted['status'].unique()

array(['jurisdiction', 'intermediary_base', 'intermediary_jurisdiction',
       'officer_base', 'officer_jurisdiction'], dtype=object)

df_weighted_grouped = pd.read_csv('combined_final_weighted_groupedby.csv')
df_weighted_grouped

	node_start	node_end	weight
0	Albania	Bahamas	4.0
1	Algeria	British Virgin Islands	2.0
2	Algeria	United Kingdom	2.0
3	Algeria	United States	1.0
4	Andorra	Bahamas	52.0
...	...	...	...
2835	Zimbabwe	Hong Kong	3.0
2836	Zimbabwe	Jersey	1.0
2837	Zimbabwe	Panama	6.0
2838	Zimbabwe	Russia	2.0
2839	Zimbabwe	United States	1.0

2840 rows × 3 columns

df_weighted_grouped.describe()

	weight
count	2840.000000
mean	194.889613
std	1729.454201
min	0.500000
25%	2.000000
50%	5.000000
75%	24.000000
max	65366.500000

Convert Data to an Undirected Edge List

df_weighted_grouped_list = df_weighted_grouped.values.tolist()
df_weighted_grouped_list

[['Albania', 'Bahamas', 4.0],
 ['Algeria', 'British Virgin Islands', 2.0],
 ['Algeria', 'United Kingdom', 2.0],
 ['Algeria', 'United States', 1.0],
 ['Andorra', 'Bahamas', 52.0],
 ['Andorra', 'British Virgin Islands', 82.0],
 ['Andorra', 'Cayman Islands', 2.0],
 ['Andorra', 'Cook Islands', 0.5],
 ['Andorra', 'Costa Rica', 6.0],
 ['Andorra', 'Hong Kong', 18.0],
 ['Andorra', 'Ireland', 1.0],
 ['Andorra', 'Latvia', 1.0],
 ['Andorra', 'Nevada', 48.0],
 ['Andorra', 'Niue', 24.0],
 ['Andorra', 'Panama', 558.0],
 ['Andorra', 'Russia', 2.0],
 ['Andorra', 'Samoa', 18.0],
 ['Andorra', 'Seychelles', 144.0],
 ['Andorra', 'United Kingdom', 2.0],
 ['Andorra', 'Uruguay', 30.0],
 ['Angola', 'Bermuda', 2.0],
 ['Angola', 'British Virgin Islands', 23.0],
 ['Angola', 'China', 1.0],
 ['Antigua and Barbuda', 'Barbados', 16.0],
 ['Antigua and Barbuda', 'British Virgin Islands', 16.5],
 ['Antigua and Barbuda', 'Canada', 1.0],
 ['Antigua and Barbuda', 'Cayman Islands', 2.0],
 ['Antigua and Barbuda', 'China', 4.0],
 ['Antigua and Barbuda', 'Hong Kong', 4.0],
 ['Antigua and Barbuda', 'Ireland', 3.0],
 ['Antigua and Barbuda', 'Niue', 4.0],
 ['Antigua and Barbuda', 'Panama', 30.0],
 ['Antigua and Barbuda', 'Russia', 1.0],
 ['Antigua and Barbuda', 'Saint Kitts and Nevis', 2.0],
 ['Antigua and Barbuda', 'Samoa', 1.5],
 ['Antigua and Barbuda', 'Seychelles', 6.0],
 ['Antigua and Barbuda', 'United Kingdom', 2.0],
 ['Antigua and Barbuda', 'United States', 1.0],
 ['Argentina', 'Antigua and Barbuda', 1.0],
 ['Argentina', 'Bahamas', 46.0],
 ['Argentina', 'Barbados', 2.0],
 ['Argentina', 'Belarus', 1.0],
 ['Argentina', 'British Virgin Islands', 189.0],
 ['Argentina', 'Canada', 1.0],
 ['Argentina', 'Cayman Islands', 6.0],
 ['Argentina', 'Costa Rica', 2.0],
 ['Argentina', 'Hong Kong', 19.0],
 ['Argentina', 'Ireland', 6.0],
 ['Argentina', 'Nevada', 8.0],
 ['Argentina', 'New Zealand', 2.0],
 ['Argentina', 'Niue', 4.0],
 ['Argentina', 'Panama', 294.0],
 ['Argentina', 'Russia', 5.0],
 ['Argentina', 'Saint Kitts and Nevis', 2.0],
 ['Argentina', 'Seychelles', 12.0],
 ['Argentina', 'Switzerland', 2.0],
 ['Argentina', 'United Kingdom', 8.0],
 ['Argentina', 'United States', 2.0],
 ['Armenia', 'British Virgin Islands', 8.0],
 ['Armenia', 'China', 1.0],
 ['Armenia', 'Hong Kong', 1.0],
 ['Armenia', 'Panama', 1.0],
 ['Armenia', 'Russia', 1.0],
 ['Armenia', 'United Kingdom', 4.0],
 ['Armenia', 'United States', 1.0],
 ['Aruba', 'Barbados', 2.0],
 ['Aruba', 'British Anguilla', 8.0],
 ['Aruba', 'British Virgin Islands', 38.0],
 ['Aruba', 'Panama', 8.0],
 ['Australia', 'Austria', 1.0],
 ['Australia', 'Bahamas', 4.0],
 ['Australia', 'Barbados', 4.0],
 ['Australia', 'Belize', 1.0],
 ['Australia', 'Bermuda', 84.0],
 ['Australia', 'British Anguilla', 2.0],
 ['Australia', 'British Virgin Islands', 646.5],
 ['Australia', 'Canada', 17.0],
 ['Australia', 'Cayman Islands', 15.0],
 ['Australia', 'China', 22.0],
 ['Australia', 'Cook Islands', 3.0],
 ['Australia', 'Cyprus', 31.0],
 ['Australia', 'Czech Republic', 1.0],
 ['Australia', 'Germany', 1.0],
 ['Australia', 'Hong Kong', 165.0],
 ['Australia', 'Ireland', 31.0],
 ['Australia', 'Isle of Man', 6.0],
 ['Australia', 'Jersey', 1.0],
 ['Australia', 'Labuan', 0.5],
 ['Australia', 'Latvia', 9.0],
 ['Australia', 'Luxembourg', 3.0],
 ['Australia', 'Malaysia', 1.0],
 ['Australia', 'Netherlands', 1.0],
 ['Australia', 'Niue', 8.0],
 ['Australia', 'Panama', 89.0],
 ['Australia', 'Russia', 107.0],
 ['Australia', 'Saint Kitts and Nevis', 13.0],
 ['Australia', 'Samoa', 1.5],
 ['Australia', 'Seychelles', 22.0],
 ['Australia', 'Singapore', 2.0],
 ['Australia', 'South Africa', 1.0],
 ['Australia', 'Sweden', 1.0],
 ['Australia', 'Ukraine', 2.0],
 ['Australia', 'United Arab Emirates', 8.0],
 ['Australia', 'United Kingdom', 151.0],
 ['Australia', 'United States', 35.0],
 ['Austria', 'Bahamas', 8.0],
 ['Austria', 'Bermuda', 8.0],
 ['Austria', 'British Anguilla', 2.0],
 ['Austria', 'British Virgin Islands', 37.5],
 ['Austria', 'Canada', 1.0],
 ['Austria', 'Cayman Islands', 4.0],
 ['Austria', 'Cyprus', 1.0],
 ['Austria', 'Hong Kong', 9.0],
 ['Austria', 'Panama', 28.0],
 ['Austria', 'Russia', 4.0],
 ['Austria', 'Saint Kitts and Nevis', 6.0],
 ['Austria', 'Samoa', 1.5],
 ['Austria', 'United Kingdom', 7.0],
 ['Austria', 'United States', 3.0],
 ['Azerbaijan', 'Bahamas', 8.0],
 ['Azerbaijan', 'British Virgin Islands', 36.0],
 ['Azerbaijan', 'Canada', 1.0],
 ['Azerbaijan', 'China', 4.0],
 ['Azerbaijan', 'Cyprus', 1.0],
 ['Azerbaijan', 'Hong Kong', 6.0],
 ['Azerbaijan', 'Ireland', 2.0],
 ['Azerbaijan', 'Netherlands', 1.0],
 ['Azerbaijan', 'Panama', 2.0],
 ['Azerbaijan', 'Russia', 7.0],
 ['Azerbaijan', 'Saint Kitts and Nevis', 5.0],
 ['Azerbaijan', 'United Kingdom', 8.0],
 ['Azerbaijan', 'United States', 2.0],
 ['Bahamas', 'Barbados', 100.0],
 ['Bahamas', 'Bermuda', 18.0],
 ['Bahamas', 'British Anguilla', 90.0],
 ['Bahamas', 'British Virgin Islands', 3645.5],
 ['Bahamas', 'Canada', 4.0],
 ['Bahamas', 'Cayman Islands', 46.0],
 ['Bahamas', 'China', 8.0],
 ['Bahamas', 'Cook Islands', 2.5],
 ['Bahamas', 'Costa Rica', 18.0],
 ['Bahamas', 'Cyprus', 8.0],
 ['Bahamas', 'Gibraltar', 1.0],
 ['Bahamas', 'Hong Kong', 52.0],
 ['Bahamas', 'Ireland', 5.0],
 ['Bahamas', 'Latvia', 3.0],
 ['Bahamas', 'Luxembourg', 3.0],
 ['Bahamas', 'Malta', 2.0],
 ['Bahamas', 'Netherlands Antilles', 2.0],
 ['Bahamas', 'Nevada', 582.0],
 ['Bahamas', 'Niue', 116.0],
 ['Bahamas', 'Panama', 4521.0],
 ['Bahamas', 'Russia', 25.0],
 ['Bahamas', 'Saint Kitts and Nevis', 2.0],
 ['Bahamas', 'Saint Lucia', 1.5],
 ['Bahamas', 'Samoa', 54.0],
 ['Bahamas', 'Seychelles', 216.0],
 ['Bahamas', 'Singapore', 2.0],
 ['Bahamas', 'United Arab Emirates', 5.0],
 ['Bahamas', 'United Kingdom', 40.0],
 ['Bahamas', 'United States', 10.0],
 ['Bahamas', 'Uruguay', 4.0],
 ['Bahamas', 'Wyoming', 30.0],
 ['Bahrain', 'Bermuda', 6.0],
 ['Bahrain', 'British Virgin Islands', 34.0],
 ['Bahrain', 'Hong Kong', 1.0],
 ['Bahrain', 'Panama', 9.0],
 ['Bahrain', 'Russia', 5.0],
 ['Bahrain', 'Ukraine', 1.0],
 ['Bahrain', 'United Arab Emirates', 3.0],
 ['Bahrain', 'United Kingdom', 5.0],
 ['Bahrain', 'United States', 1.0],
 ['Bangladesh', 'Bermuda', 2.0],
 ['Bangladesh', 'British Virgin Islands', 33.0],
 ['Bangladesh', 'Cyprus', 4.0],
 ['Bangladesh', 'Hong Kong', 12.0],
 ['Bangladesh', 'Ireland', 2.0],
 ['Bangladesh', 'Latvia', 1.0],
 ['Bangladesh', 'Russia', 8.0],
 ['Bangladesh', 'United Kingdom', 8.0],
 ['Barbados', 'Bahamas', 4.0],
 ['Barbados', 'Bermuda', 16.0],
 ['Barbados', 'British Virgin Islands', 39.0],
 ['Barbados', 'Cayman Islands', 8.0],
 ['Barbados', 'China', 1.0],
 ['Barbados', 'Cyprus', 1.0],
 ['Barbados', 'Hong Kong', 1.0],
 ['Barbados', 'Panama', 37.0],
 ['Barbados', 'Russia', 3.0],
 ['Barbados', 'Singapore', 1.0],
 ['Barbados', 'United Kingdom', 3.0],
 ['Belarus', 'Austria', 1.0],
 ['Belarus', 'British Virgin Islands', 47.5],
 ['Belarus', 'China', 2.0],
 ['Belarus', 'Hong Kong', 7.0],
 ['Belarus', 'Ireland', 1.0],
 ['Belarus', 'Luxembourg', 2.0],
 ['Belarus', 'Mauritius', 1.0],
 ['Belarus', 'Panama', 4.0],
 ['Belarus', 'Russia', 3.0],
 ['Belarus', 'Singapore', 1.0],
 ['Belarus', 'Ukraine', 1.0],
 ['Belarus', 'United Arab Emirates', 1.0],
 ['Belarus', 'United Kingdom', 5.0],
 ['Belarus', 'United States', 4.0],
 ['Belgium', 'Bahamas', 16.0],
 ['Belgium', 'Barbados', 2.0],
 ['Belgium', 'Bermuda', 2.0],
 ['Belgium', 'British Anguilla', 2.0],
 ['Belgium', 'British Virgin Islands', 94.0],
 ['Belgium', 'Canada', 4.0],
 ['Belgium', 'Cayman Islands', 1.0],
 ['Belgium', 'China', 5.0],
 ['Belgium', 'Cyprus', 1.0],
 ['Belgium', 'Hong Kong', 13.0],
 ['Belgium', 'Ireland', 5.0],
 ['Belgium', 'Isle of Man', 2.0],
 ['Belgium', 'Latvia', 4.0],
 ['Belgium', 'Luxembourg', 1.0],
 ['Belgium', 'Niue', 2.0],
 ['Belgium', 'Panama', 53.0],
 ['Belgium', 'Russia', 16.0],
 ['Belgium', 'Saint Kitts and Nevis', 4.0],
 ['Belgium', 'Samoa', 2.0],
 ['Belgium', 'Seychelles', 11.0],
 ['Belgium', 'United Arab Emirates', 1.0],
 ['Belgium', 'United Kingdom', 18.0],
 ['Belgium', 'United States', 8.0],
 ['Belize', 'Australia', 1.0],
 ['Belize', 'Bahamas', 500.0],
 ['Belize', 'Barbados', 6.0],
 ['Belize', 'British Anguilla', 38.0],
 ['Belize', 'British Virgin Islands', 414.5],
 ['Belize', 'Canada', 4.0],
 ['Belize', 'Cayman Islands', 2.0],
 ['Belize', 'China', 11.0],
 ['Belize', 'Cyprus', 4.0],
 ['Belize', 'Hong Kong', 118.0],
 ['Belize', 'Ireland', 10.0],
 ['Belize', 'Mauritius', 2.0],
 ['Belize', 'Netherlands', 2.0],
 ['Belize', 'Niue', 456.0],
 ['Belize', 'Panama', 850.0],
 ['Belize', 'Russia', 15.0],
 ['Belize', 'Saint Kitts and Nevis', 3.0],
 ['Belize', 'Samoa', 90.0],
 ['Belize', 'Seychelles', 350.0],
 ['Belize', 'United Kingdom', 22.0],
 ['Belize', 'United States', 2.0],
 ['Belize', 'Wyoming', 2.0],
 ['Benin', 'British Virgin Islands', 1.0],
 ['Bermuda', 'Bahamas', 42.0],
 ['Bermuda', 'Barbados', 74.0],
 ['Bermuda', 'British Virgin Islands', 360.5],
 ['Bermuda', 'Canada', 2.0],
 ['Bermuda', 'Cayman Islands', 34.0],
 ['Bermuda', 'China', 5.0],
 ['Bermuda', 'Cook Islands', 2.0],
 ['Bermuda', 'Cyprus', 7.0],
 ['Bermuda', 'Czech Republic', 4.0],
 ['Bermuda', 'Hong Kong', 29.0],
 ['Bermuda', 'Ireland', 4.0],
 ['Bermuda', 'Jersey', 7.0],
 ['Bermuda', 'Latvia', 5.0],
 ['Bermuda', 'Liberia', 8.0],
 ['Bermuda', 'Liechtenstein', 10.0],
 ['Bermuda', 'Netherlands', 11.0],
 ['Bermuda', 'Netherlands Antilles', 2.0],
 ['Bermuda', 'Panama', 54.0],
 ['Bermuda', 'Russia', 20.0],
 ['Bermuda', 'Saint Kitts and Nevis', 6.0],
 ['Bermuda', 'Samoa', 1.5],
 ['Bermuda', 'Seychelles', 3.0],
 ['Bermuda', 'Singapore', 3.0],
 ['Bermuda', 'South Africa', 1.0],
 ['Bermuda', 'Sweden', 1.0],
 ['Bermuda', 'Turks and Caicos Islands', 2.0],
 ['Bermuda', 'United Arab Emirates', 1.0],
 ['Bermuda', 'United Kingdom', 29.0],
 ['Bermuda', 'United States', 47.0],
 ['Bermuda', 'Vanuatu', 1.0],
 ['Bhutan', 'British Virgin Islands', 1.0],
 ['Bhutan', 'Hong Kong', 1.0],
 ['Bolivia', 'British Virgin Islands', 4.0],
 ['Bolivia', 'Hong Kong', 4.0],
 ['Bolivia', 'Panama', 182.0],
 ['Bosnia and Herzegovina', 'British Virgin Islands', 2.0],
 ['Bosnia and Herzegovina', 'China', 1.0],
 ['Bosnia and Herzegovina', 'Hong Kong', 1.0],
 ['Botswana', 'British Virgin Islands', 5.0],
 ['Botswana', 'Panama', 2.0],
 ['Botswana', 'Seychelles', 6.0],
 ['Botswana', 'United Kingdom', 1.0],
 ['Brazil', 'Bahamas', 66.0],
 ['Brazil', 'Barbados', 4.0],
 ['Brazil', 'Bermuda', 8.0],
 ['Brazil', 'British Anguilla', 28.0],
 ['Brazil', 'British Virgin Islands', 986.5],
 ['Brazil', 'Canada', 1.0],
 ['Brazil', 'Cayman Islands', 42.0],
 ['Brazil', 'China', 7.0],
 ['Brazil', 'Cyprus', 2.0],
 ['Brazil', 'Hong Kong', 11.0],
 ['Brazil', 'Ireland', 3.0],
 ['Brazil', 'Isle of Man', 6.0],
 ['Brazil', 'Nevada', 264.0],
 ['Brazil', 'New Zealand', 2.0],
 ['Brazil', 'Niue', 182.0],
 ['Brazil', 'Panama', 1044.0],
 ['Brazil', 'Russia', 10.0],
 ['Brazil', 'Samoa', 86.0],
 ['Brazil', 'Seychelles', 159.0],
 ['Brazil', 'Singapore', 1.0],
 ['Brazil', 'United Arab Emirates', 2.0],
 ['Brazil', 'United Kingdom', 25.0],
 ['Brazil', 'United States', 1.0],
 ['Brazil', 'Uruguay', 2.0],
 ['Brazil', 'Wyoming', 8.0],
 ['British Anguilla', 'Barbados', 14.0],
 ['British Anguilla', 'British Anguilla', 42.0],
 ['British Anguilla', 'British Virgin Islands', 16.0],
 ['British Anguilla', 'China', 2.0],
 ['British Anguilla', 'Hong Kong', 6.0],
 ['British Anguilla', 'Ireland', 1.0],
 ['British Anguilla', 'Panama', 1.0],
 ['British Anguilla', 'Russia', 2.0],
 ['British Anguilla', 'United Kingdom', 4.0],
 ['British Virgin Islands', 'Antigua and Barbuda', 7.0],
 ['British Virgin Islands', 'Austria', 3.0],
 ['British Virgin Islands', 'Bahamas', 290.0],
 ['British Virgin Islands', 'Barbados', 2106.0],
 ['British Virgin Islands', 'Belarus', 1.0],
 ['British Virgin Islands', 'Belize', 9.0],
 ['British Virgin Islands', 'Bermuda', 26.0],
 ['British Virgin Islands', 'British Anguilla', 2.0],
 ['British Virgin Islands', 'Canada', 96.0],
 ['British Virgin Islands', 'Cayman Islands', 20.0],
 ['British Virgin Islands', 'China', 174.0],
 ['British Virgin Islands', 'Cook Islands', 152.0],
 ['British Virgin Islands', 'Croatia', 1.0],
 ['British Virgin Islands', 'Curaçao', 4.0],
 ['British Virgin Islands', 'Cyprus', 198.0],
 ['British Virgin Islands', 'Czech Republic', 5.0],
 ['British Virgin Islands', 'Germany', 2.0],
 ['British Virgin Islands', 'Gibraltar', 4.0],
 ['British Virgin Islands', 'Hong Kong', 896.0],
 ['British Virgin Islands', 'Ireland', 184.0],
 ['British Virgin Islands', 'Isle of Man', 4.0],
 ['British Virgin Islands', 'Israel', 2.0],
 ['British Virgin Islands', 'Jersey', 27.0],
 ['British Virgin Islands', 'Labuan', 0.5],
 ['British Virgin Islands', 'Latvia', 70.0],
 ['British Virgin Islands', 'Luxembourg', 20.0],
 ['British Virgin Islands', 'Malaysia', 1.0],
 ['British Virgin Islands', 'Malta', 5.0],
 ['British Virgin Islands', 'Marshall Islands', 1.5],
 ['British Virgin Islands', 'Mauritius', 7.0],
 ['British Virgin Islands', 'Netherlands', 12.0],
 ['British Virgin Islands', 'Nevada', 4.0],
 ['British Virgin Islands', 'Niue', 4.0],
 ['British Virgin Islands', 'Panama', 513.0],
 ['British Virgin Islands', 'Russia', 608.0],
 ['British Virgin Islands', 'Saint Kitts and Nevis', 79.0],
 ['British Virgin Islands', 'Samoa', 4.5],
 ['British Virgin Islands', 'Seychelles', 96.5],
 ['British Virgin Islands', 'Singapore', 37.5],
 ['British Virgin Islands', 'South Africa', 3.0],
 ['British Virgin Islands', 'State of Delaware', 2.0],
 ['British Virgin Islands', 'Sweden', 3.0],
 ['British Virgin Islands', 'Switzerland', 11.0],
 ['British Virgin Islands', 'Thailand', 1.0],
 ['British Virgin Islands', 'Turks and Caicos Islands', 2.0],
 ['British Virgin Islands', 'Ukraine', 8.0],
 ['British Virgin Islands', 'United Arab Emirates', 66.0],
 ['British Virgin Islands', 'United Kingdom', 918.0],
 ['British Virgin Islands', 'United States', 248.0],
 ['Brunei', 'British Virgin Islands', 33.5],
 ['Brunei', 'China', 1.0],
 ['Brunei', 'Cyprus', 4.0],
 ['Brunei', 'Hong Kong', 11.0],
 ['Brunei', 'Russia', 9.0],
 ['Brunei', 'Saint Kitts and Nevis', 2.0],
 ['Brunei', 'Seychelles', 2.0],
 ['Brunei', 'United Kingdom', 7.0],
 ['Brunei', 'United States', 1.0],
 ['Bulgaria', 'Bahamas', 4.0],
 ['Bulgaria', 'British Virgin Islands', 49.0],
 ['Bulgaria', 'China', 1.0],
 ['Bulgaria', 'Hong Kong', 2.0],
 ['Bulgaria', 'Ireland', 1.0],
 ['Bulgaria', 'Niue', 2.0],
 ['Bulgaria', 'Panama', 11.0],
 ['Bulgaria', 'Russia', 2.0],
 ['Bulgaria', 'Seychelles', 44.0],
 ['Bulgaria', 'United Kingdom', 2.0],
 ['Bulgaria', 'United States', 1.0],
 ['Cambodia', 'British Virgin Islands', 8.0],
 ['Cambodia', 'China', 1.0],
 ['Cambodia', 'Hong Kong', 3.0],
 ['Cambodia', 'Ireland', 2.0],
 ['Cambodia', 'Panama', 1.0],
 ['Cambodia', 'Russia', 2.0],
 ['Cambodia', 'United Kingdom', 2.0],
 ['Cameroon', 'Seychelles', 2.0],
 ['Canada', 'Antigua and Barbuda', 5.0],
 ['Canada', 'Bahamas', 72.0],
 ['Canada', 'Barbados', 264.0],
 ['Canada', 'Belize', 2.0],
 ['Canada', 'Bermuda', 158.0],
 ['Canada', 'British Anguilla', 4.0],
 ['Canada', 'British Virgin Islands', 875.5],
 ['Canada', 'Cayman Islands', 129.5],
 ['Canada', 'China', 29.0],
 ['Canada', 'Cook Islands', 4.5],
 ['Canada', 'Curaçao', 1.0],
 ['Canada', 'Cyprus', 22.0],
 ['Canada', 'Gibraltar', 1.0],
 ['Canada', 'Hong Kong', 124.0],
 ['Canada', 'Indonesia', 1.0],
 ['Canada', 'Ireland', 29.0],
 ['Canada', 'Isle of Man', 10.0],
 ['Canada', 'Jersey', 1.0],
 ['Canada', 'Labuan', 1.0],
 ['Canada', 'Latvia', 10.0],
 ['Canada', 'Luxembourg', 5.0],
 ['Canada', 'Malaysia', 2.0],
 ['Canada', 'Mauritius', 1.0],
 ['Canada', 'Netherlands', 1.0],
 ['Canada', 'Nevada', 4.0],
 ['Canada', 'Niue', 62.0],
 ['Canada', 'Panama', 201.0],
 ['Canada', 'Russia', 95.0],
 ['Canada', 'Saint Kitts and Nevis', 9.0],
 ['Canada', 'Samoa', 12.0],
 ['Canada', 'Seychelles', 47.0],
 ['Canada', 'Singapore', 14.0],
 ['Canada', 'South Africa', 1.0],
 ['Canada', 'Switzerland', 1.0],
 ['Canada', 'Ukraine', 2.0],
 ['Canada', 'United Arab Emirates', 8.0],
 ['Canada', 'United Kingdom', 117.0],
 ['Canada', 'United States', 38.0],
 ['Cayman Islands', 'Antigua and Barbuda', 9.0],
 ['Cayman Islands', 'Aruba', 4.0],
 ['Cayman Islands', 'Bahamas', 86.0],
 ['Cayman Islands', 'Barbados', 226.0],
 ['Cayman Islands', 'Bermuda', 42.0],
 ['Cayman Islands', 'British Virgin Islands', 2446.0],
 ['Cayman Islands', 'Canada', 8.0],
 ['Cayman Islands', 'China', 15.0],
 ['Cayman Islands', 'Cook Islands', 72.5],
 ['Cayman Islands', 'Cyprus', 31.0],
 ['Cayman Islands', 'Gibraltar', 2.0],
 ['Cayman Islands', 'Grenada', 2.0],
 ['Cayman Islands', 'Guernsey', 18.0],
 ['Cayman Islands', 'Hong Kong', 259.0],
 ['Cayman Islands', 'Ireland', 21.0],
 ['Cayman Islands', 'Isle of Man', 16.0],
 ['Cayman Islands', 'Jersey', 7.0],
 ['Cayman Islands', 'Latvia', 10.0],
 ['Cayman Islands', 'Liberia', 2.0],
 ['Cayman Islands', 'Liechtenstein', 2.0],
 ['Cayman Islands', 'Luxembourg', 8.0],
 ['Cayman Islands', 'Malta', 2.0],
 ['Cayman Islands', 'Marshall Islands', 2.0],
 ['Cayman Islands', 'Netherlands', 4.0],
 ['Cayman Islands', 'Netherlands Antilles', 4.0],
 ['Cayman Islands', 'New Zealand', 4.0],
 ['Cayman Islands', 'Niue', 2.0],
 ['Cayman Islands', 'Panama', 134.0],
 ['Cayman Islands', 'Russia', 71.0],
 ['Cayman Islands', 'Saint Kitts and Nevis', 195.0],
 ['Cayman Islands', 'Saint Vincent and the Grenadines', 24.0],
 ['Cayman Islands', 'Samoa', 1709.0],
 ['Cayman Islands', 'Seychelles', 17.0],
 ['Cayman Islands', 'Singapore', 13.0],
 ['Cayman Islands', 'State of Delaware', 232.0],
 ['Cayman Islands', 'Sweden', 1.0],
 ['Cayman Islands', 'Switzerland', 3.0],
 ['Cayman Islands', 'Turks and Caicos Islands', 3.0],
 ['Cayman Islands', 'US Virgin Islands', 2.0],
 ['Cayman Islands', 'Ukraine', 1.0],
 ['Cayman Islands', 'United Arab Emirates', 5.0],
 ['Cayman Islands', 'United Kingdom', 98.0],
 ['Cayman Islands', 'United States', 82.0],
 ['Central African Republic', 'British Virgin Islands', 2.0],
 ['Chad', 'British Virgin Islands', 20.0],
 ['Chad', 'Panama', 2.0],
 ['Chile', 'Bahamas', 28.0],
 ['Chile', 'Barbados', 2.0],
 ['Chile', 'Bermuda', 2.0],
 ['Chile', 'British Anguilla', 4.0],
 ['Chile', 'British Virgin Islands', 93.0],
 ['Chile', 'Canada', 1.0],
 ['Chile', 'China', 3.0],
 ['Chile', 'Costa Rica', 2.0],
 ['Chile', 'Cyprus', 3.0],
 ['Chile', 'Hong Kong', 7.0],
 ['Chile', 'Ireland', 2.0],
 ['Chile', 'Nevada', 12.0],
 ['Chile', 'Panama', 173.0],
 ['Chile', 'Russia', 10.0],
 ['Chile', 'Seychelles', 4.0],
 ['Chile', 'United Kingdom', 6.0],
 ['Chile', 'United States', 3.0],
 ['Chile', 'Wyoming', 2.0],
 ['China', 'Antigua and Barbuda', 26.0],
 ['China', 'Austria', 10.0],
 ['China', 'Bahamas', 12.0],
 ['China', 'Barbados', 2.0],
 ['China', 'Belarus', 1.0],
 ['China', 'Belize', 23.0],
 ['China', 'Bermuda', 154.0],
 ['China', 'British Anguilla', 296.0],
 ['China', 'British Virgin Islands', 11138.0],
 ['China', 'Canada', 150.0],
 ['China', 'Cayman Islands', 296.5],
 ['China', 'Cook Islands', 3.0],
 ['China', 'Croatia', 2.0],
 ['China', 'Curaçao', 4.0],
 ['China', 'Cyprus', 396.0],
 ['China', 'Czech Republic', 13.0],
 ['China', 'France', 1.0],
 ['China', 'Georgia', 2.0],
 ['China', 'Germany', 4.0],
 ['China', 'Gibraltar', 10.0],
 ['China', 'Hong Kong', 2046.0],
 ['China', 'Ireland', 397.0],
 ['China', 'Isle of Man', 2.0],
 ['China', 'Israel', 3.0],
 ['China', 'Jersey', 48.0],
 ['China', 'Kazakhstan', 1.0],
 ['China', 'Labuan', 0.5],
 ['China', 'Latvia', 165.0],
 ['China', 'Luxembourg', 47.0],
 ['China', 'Malaysia', 2.0],
 ['China', 'Malta', 12.0],
 ['China', 'Mauritius', 18.0],
 ['China', 'Netherlands', 14.0],
 ['China', 'Nevada', 18.0],
 ['China', 'Niue', 24.0],
 ['China', 'Panama', 670.0],
 ['China', 'Russia', 1470.0],
 ['China', 'Saint Kitts and Nevis', 135.0],
 ['China', 'Samoa', 748.5],
 ['China', 'Serbia', 1.0],
 ['China', 'Seychelles', 1486.0],
 ['China', 'Singapore', 74.5],
 ['China', 'South Africa', 1.0],
 ['China', 'Sweden', 7.0],
 ['China', 'Switzerland', 25.0],
 ['China', 'Thailand', 1.0],
 ['China', 'Turks and Caicos Islands', 2.0],
 ['China', 'US Virgin Islands', 1.0],
 ['China', 'Ukraine', 17.0],
 ['China', 'United Arab Emirates', 101.0],
 ['China', 'United Kingdom', 2035.0],
 ['China', 'United States', 566.0],
 ['China', 'Vanuatu', 1.0],
 ['Colombia', 'Bahamas', 44.0],
 ['Colombia', 'Belize', 38.0],
 ['Colombia', 'Bermuda', 4.0],
 ['Colombia', 'British Anguilla', 118.0],
 ['Colombia', 'British Virgin Islands', 541.0],
 ['Colombia', 'Cayman Islands', 6.0],
 ['Colombia', 'Costa Rica', 84.0],
 ['Colombia', 'Cyprus', 4.0],
 ['Colombia', 'Hong Kong', 5.0],
 ['Colombia', 'Ireland', 1.0],
 ['Colombia', 'Latvia', 1.0],
 ['Colombia', 'Luxembourg', 1.0],
 ['Colombia', 'Malta', 2.0],
 ['Colombia', 'Nevada', 20.0],
 ['Colombia', 'New Zealand', 6.0],
 ['Colombia', 'Niue', 4.0],
 ['Colombia', 'Panama', 2715.0],
 ['Colombia', 'Russia', 4.0],
 ['Colombia', 'Saint Kitts and Nevis', 6.0],
 ['Colombia', 'Samoa', 12.0],
 ['Colombia', 'Seychelles', 36.0],
 ['Colombia', 'United Kingdom', 104.0],
 ['Colombia', 'United States', 1.0],
 ['Colombia', 'Uruguay', 2.0],
 ['Congo', 'British Virgin Islands', 3.0],
 ['Congo', 'Ireland', 1.0],
 ['Congo', 'Russia', 1.0],
 ['Congo', 'Singapore', 1.0],
 ['Cook Islands', 'Antigua and Barbuda', 1.0],
 ['Cook Islands', 'Australia', 1.0],
 ['Cook Islands', 'Austria', 1.0],
 ['Cook Islands', 'Bermuda', 2.0],
 ['Cook Islands', 'British Virgin Islands', 317.5],
 ['Cook Islands', 'Canada', 11.0],
 ['Cook Islands', 'China', 7.0],
 ['Cook Islands', 'Cyprus', 21.0],
 ['Cook Islands', 'Gibraltar', 1.0],
 ['Cook Islands', 'Hong Kong', 90.0],
 ['Cook Islands', 'Indonesia', 1.0],
 ['Cook Islands', 'Ireland', 23.0],
 ['Cook Islands', 'Jersey', 1.0],
 ['Cook Islands', 'Latvia', 6.0],
 ['Cook Islands', 'Netherlands', 1.0],
 ['Cook Islands', 'Niue', 2.0],
 ['Cook Islands', 'Panama', 34.0],
 ['Cook Islands', 'Russia', 60.0],
 ['Cook Islands', 'Saint Kitts and Nevis', 13.0],
 ['Cook Islands', 'Samoa', 2.0],
 ['Cook Islands', 'Seychelles', 7.0],
 ['Cook Islands', 'Singapore', 6.0],
 ['Cook Islands', 'Switzerland', 3.0],
 ['Cook Islands', 'Turks and Caicos Islands', 1.0],
 ['Cook Islands', 'United Arab Emirates', 8.0],
 ['Cook Islands', 'United Kingdom', 106.0],
 ['Cook Islands', 'United States', 24.0],
 ['Costa Rica', 'Bahamas', 64.0],
 ['Costa Rica', 'British Anguilla', 22.0],
 ['Costa Rica', 'British Virgin Islands', 288.0],
 ['Costa Rica', 'Cayman Islands', 4.0],
 ['Costa Rica', 'China', 1.0],
 ['Costa Rica', 'Hong Kong', 11.0],
 ['Costa Rica', 'Isle of Man', 2.0],
 ['Costa Rica', 'Nevada', 4.0],
 ['Costa Rica', 'Niue', 8.0],
 ['Costa Rica', 'Panama', 1373.0],
 ['Costa Rica', 'Russia', 1.0],
 ['Costa Rica', 'Samoa', 5.5],
 ['Costa Rica', 'Seychelles', 4.0],
 ['Costa Rica', 'Singapore', 1.0],
 ['Costa Rica', 'United Kingdom', 3.0],
 ['Croatia', 'Barbados', 2.0],
 ['Croatia', 'British Virgin Islands', 11.5],
 ['Croatia', 'Cyprus', 1.0],
 ['Croatia', 'Ireland', 3.0],
 ['Croatia', 'Panama', 3.0],
 ['Croatia', 'Russia', 2.0],
 ['Croatia', 'Saint Kitts and Nevis', 1.0],
 ['Cuba', 'Barbados', 2.0],
 ['Cuba', 'British Virgin Islands', 16.0],
 ['Cuba', 'Cayman Islands', 2.0],
 ['Cuba', 'Panama', 6.0],
 ['Curaçao', 'Barbados', 38.0],
 ['Curaçao', 'Bermuda', 2.0],
 ['Curaçao', 'British Virgin Islands', 35.5],
 ['Curaçao', 'China', 3.0],
 ['Curaçao', 'Cyprus', 2.0],
 ['Curaçao', 'Hong Kong', 3.0],
 ['Curaçao', 'Ireland', 1.0],
 ['Curaçao', 'Jersey', 1.0],
 ['Curaçao', 'Panama', 18.0],
 ['Curaçao', 'Russia', 2.0],
 ['Curaçao', 'Seychelles', 24.0],
 ['Curaçao', 'Singapore', 2.5],
 ['Curaçao', 'United Arab Emirates', 2.0],
 ['Curaçao', 'United Kingdom', 2.0],
 ['Curaçao', 'United States', 3.0],
 ['Cyprus', 'Antigua and Barbuda', 4.0],
 ['Cyprus', 'Bahamas', 1301.0],
 ['Cyprus', 'Barbados', 10.0],
 ['Cyprus', 'Belize', 29.0],
 ['Cyprus', 'Bermuda', 16.0],
 ['Cyprus', 'British Anguilla', 10.0],
 ['Cyprus', 'British Virgin Islands', 4498.5],
 ['Cyprus', 'Canada', 22.0],
 ['Cyprus', 'Cayman Islands', 21.0],
 ['Cyprus', 'China', 132.0],
 ['Cyprus', 'Cook Islands', 3.0],
 ['Cyprus', 'Croatia', 1.0],
 ['Cyprus', 'Czech Republic', 1.0],
 ['Cyprus', 'Germany', 1.0],
 ['Cyprus', 'Gibraltar', 1.0],
 ['Cyprus', 'Hong Kong', 208.0],
 ['Cyprus', 'Ireland', 53.0],
 ['Cyprus', 'Isle of Man', 8.0],
 ['Cyprus', 'Jersey', 7.0],
 ['Cyprus', 'Labuan', 1.0],
 ['Cyprus', 'Latvia', 9.0],
 ['Cyprus', 'Luxembourg', 2.0],
 ['Cyprus', 'Malaysia', 2.0],
 ['Cyprus', 'Mauritius', 3.0],
 ['Cyprus', 'Netherlands', 4.0],
 ['Cyprus', 'Nevada', 2.0],
 ['Cyprus', 'Niue', 626.0],
 ['Cyprus', 'Panama', 705.0],
 ['Cyprus', 'Russia', 146.0],
 ['Cyprus', 'Saint Kitts and Nevis', 18.0],
 ['Cyprus', 'Saint Lucia', 1.5],
 ['Cyprus', 'Samoa', 23.5],
 ['Cyprus', 'Seychelles', 904.0],
 ['Cyprus', 'Singapore', 12.0],
 ['Cyprus', 'Switzerland', 4.0],
 ['Cyprus', 'Ukraine', 2.0],
 ['Cyprus', 'United Arab Emirates', 5.0],
 ['Cyprus', 'United Kingdom', 192.0],
 ['Cyprus', 'United States', 50.0],
 ['Czech Republic', 'Bahamas', 4.0],
 ['Czech Republic', 'Barbados', 2.0],
 ['Czech Republic', 'Belize', 1.0],
 ['Czech Republic', 'British Virgin Islands', 81.0],
 ['Czech Republic', 'Canada', 1.0],
 ['Czech Republic', 'China', 3.0],
 ['Czech Republic', 'Cyprus', 6.0],
 ['Czech Republic', 'Hong Kong', 9.0],
 ['Czech Republic', 'Ireland', 1.0],
 ['Czech Republic', 'Isle of Man', 2.0],
 ['Czech Republic', 'Netherlands', 1.0],
 ['Czech Republic', 'Panama', 14.0],
 ['Czech Republic', 'Russia', 4.0],
 ['Czech Republic', 'Samoa', 2.0],
 ['Czech Republic', 'Seychelles', 2.0],
 ['Czech Republic', 'Singapore', 1.0],
 ['Czech Republic', 'United Kingdom', 3.0],
 ["Côte d'Ivoire", 'British Virgin Islands', 22.0],
 ["Côte d'Ivoire", 'Cyprus', 1.0],
 ["Côte d'Ivoire", 'Hong Kong', 2.0],
 ["Côte d'Ivoire", 'Ireland', 1.0],
 ["Côte d'Ivoire", 'Nevada', 2.0],
 ["Côte d'Ivoire", 'Niue', 2.0],
 ["Côte d'Ivoire", 'Panama', 14.0],
 ["Côte d'Ivoire", 'Samoa', 4.0],
 ["Côte d'Ivoire", 'United Kingdom', 1.0],
 ['Denmark', 'Bahamas', 8.0],
 ['Denmark', 'Barbados', 2.0],
 ['Denmark', 'Bermuda', 4.0],
 ['Denmark', 'British Virgin Islands', 17.0],
 ['Denmark', 'Hong Kong', 5.0],
 ['Denmark', 'Latvia', 1.0],
 ['Denmark', 'Panama', 9.0],
 ['Denmark', 'Russia', 2.0],
 ['Denmark', 'Saint Kitts and Nevis', 1.0],
 ['Denmark', 'Seychelles', 5.0],
 ['Denmark', 'Ukraine', 1.0],
 ['Denmark', 'United Kingdom', 1.0],
 ['Denmark', 'United States', 1.0],
 ['Djibouti', 'British Virgin Islands', 1.0],
 ['Djibouti', 'Canada', 2.0],
 ['Djibouti', 'China', 1.0],
 ['Djibouti', 'Panama', 2.0],
 ['Dominica', 'Barbados', 2.0],
 ['Dominica', 'British Virgin Islands', 9.0],
 ['Dominica', 'Hong Kong', 2.0],
 ['Dominica', 'Israel', 1.0],
 ['Dominica', 'Jersey', 1.0],
 ['Dominica', 'Panama', 124.0],
 ['Dominican Republic', 'Bahamas', 16.0],
 ['Dominican Republic', 'Barbados', 6.0],
 ['Dominican Republic', 'British Anguilla', 48.0],
 ['Dominican Republic', 'British Virgin Islands', 275.0],
 ['Dominican Republic', 'Canada', 2.0],
 ['Dominican Republic', 'Cayman Islands', 10.0],
 ['Dominican Republic', 'Cyprus', 1.0],
 ['Dominican Republic', 'Hong Kong', 4.0],
 ['Dominican Republic', 'Ireland', 1.0],
 ['Dominican Republic', 'Panama', 624.0],
 ['Dominican Republic', 'Saint Kitts and Nevis', 1.0],
 ['Dominican Republic', 'Samoa', 2.0],
 ['Dominican Republic', 'Seychelles', 23.0],
 ['Dominican Republic', 'United Kingdom', 4.0],
 ['Dominican Republic', 'United States', 2.0],
 ['Ecuador', 'Bahamas', 98.0],
 ['Ecuador', 'Barbados', 4.0],
 ['Ecuador', 'Belize', 10.0],
 ['Ecuador', 'Bermuda', 2.0],
 ['Ecuador', 'British Anguilla', 292.0],
 ['Ecuador', 'British Virgin Islands', 507.0],
 ['Ecuador', 'Canada', 1.0],
 ['Ecuador', 'Cayman Islands', 2.0],
 ['Ecuador', 'Costa Rica', 16.0],
 ['Ecuador', 'Hong Kong', 7.0],
 ['Ecuador', 'Ireland', 1.0],
 ['Ecuador', 'Nevada', 398.0],
 ['Ecuador', 'New Zealand', 18.0],
 ['Ecuador', 'Niue', 52.0],
 ['Ecuador', 'Panama', 2203.0],
 ['Ecuador', 'Samoa', 40.0],
 ['Ecuador', 'Seychelles', 52.0],
 ['Ecuador', 'United Kingdom', 14.0],
 ['Ecuador', 'Uruguay', 4.0],
 ['Egypt', 'Bahamas', 10.0],
 ['Egypt', 'Barbados', 2.0],
 ['Egypt', 'Bermuda', 6.0],
 ['Egypt', 'British Virgin Islands', 55.0],
 ['Egypt', 'Cayman Islands', 2.0],
 ['Egypt', 'China', 1.0],
 ['Egypt', 'Cyprus', 2.0],
 ['Egypt', 'Hong Kong', 5.0],
 ['Egypt', 'Panama', 44.0],
 ['Egypt', 'Russia', 8.0],
 ['Egypt', 'Seychelles', 4.0],
 ['Egypt', 'United Kingdom', 4.0],
 ['El Salvador', 'Bahamas', 12.0],
 ['El Salvador', 'Barbados', 4.0],
 ['El Salvador', 'British Anguilla', 2.0],
 ['El Salvador', 'British Virgin Islands', 40.0],
 ['El Salvador', 'China', 1.0],
 ['El Salvador', 'Hong Kong', 2.0],
 ['El Salvador', 'Nevada', 14.0],
 ['El Salvador', 'Niue', 2.0],
 ['El Salvador', 'Panama', 391.0],
 ['El Salvador', 'Seychelles', 10.0],
 ['Estonia', 'Bahamas', 228.0],
 ['Estonia', 'British Anguilla', 2.0],
 ['Estonia', 'British Virgin Islands', 1295.5],
 ['Estonia', 'China', 4.0],
 ['Estonia', 'Cyprus', 4.0],
 ['Estonia', 'Hong Kong', 1.0],
 ['Estonia', 'Isle of Man', 2.0],
 ['Estonia', 'Malta', 2.0],
 ['Estonia', 'Niue', 136.0],
 ['Estonia', 'Panama', 28.0],
 ['Estonia', 'Russia', 1.0],
 ['Estonia', 'Samoa', 6.0],
 ['Estonia', 'Seychelles', 68.0],
 ['Fiji', 'British Virgin Islands', 12.0],
 ['Fiji', 'Canada', 1.0],
 ['Fiji', 'Cook Islands', 2.0],
 ['Fiji', 'Hong Kong', 2.0],
 ['Fiji', 'Ireland', 2.0],
 ['Fiji', 'Latvia', 1.0],
 ['Fiji', 'Russia', 3.0],
 ['Fiji', 'Singapore', 1.5],
 ['Fiji', 'United Kingdom', 2.0],
 ['Fiji', 'United States', 2.0],
 ['Finland', 'Bahamas', 64.0],
 ['Finland', 'Belize', 1.0],
 ['Finland', 'British Virgin Islands', 71.5],
 ['Finland', 'Canada', 1.0],
 ['Finland', 'Cayman Islands', 14.0],
 ['Finland', 'China', 3.0],
 ['Finland', 'Cyprus', 2.0],
 ['Finland', 'Hong Kong', 4.0],
 ['Finland', 'Niue', 2.0],
 ['Finland', 'Panama', 8.0],
 ['Finland', 'Russia', 2.0],
 ['Finland', 'Saint Kitts and Nevis', 1.0],
 ['Finland', 'Seychelles', 8.0],
 ['Finland', 'Singapore', 1.0],
 ['Finland', 'United Kingdom', 7.0],
 ['Finland', 'United States', 2.0],
 ['France', 'Antigua and Barbuda', 1.0],
 ['France', 'Bahamas', 46.0],
 ['France', 'Barbados', 4.0],
 ['France', 'Bermuda', 50.0],
 ['France', 'British Anguilla', 20.0],
 ['France', 'British Virgin Islands', 408.5],
 ['France', 'Canada', 1.0],
 ['France', 'Cayman Islands', 12.0],
 ['France', 'China', 4.0],
 ['France', 'Cook Islands', 3.0],
 ['France', 'Cyprus', 8.5],
 ['France', 'Hong Kong', 34.0],
 ['France', 'Indonesia', 1.0],
 ['France', 'Ireland', 8.0],
 ['France', 'Isle of Man', 2.0],
 ['France', 'Jersey', 1.0],
 ['France', 'Labuan', 0.5],
 ['France', 'Latvia', 2.0],
 ['France', 'Malaysia', 1.0],
 ['France', 'Nevada', 2.0],
 ['France', 'Niue', 36.0],
 ['France', 'Panama', 168.0],
 ['France', 'Russia', 35.0],
 ['France', 'Saint Kitts and Nevis', 3.0],
 ['France', 'Samoa', 7.5],
 ['France', 'Seychelles', 33.0],
 ['France', 'Singapore', 2.0],
 ['France', 'United Arab Emirates', 1.0],
 ['France', 'United Kingdom', 38.0],
 ['France', 'United States', 10.0],
 ['French Guiana', 'British Virgin Islands', 40.0],
 ['French Guiana', 'Cayman Islands', 16.0],
 ['French Guiana', 'Guernsey', 4.0],
 ['French Polynesia', 'British Virgin Islands', 14.0],
 ['French Polynesia', 'Hong Kong', 4.0],
 ['French Polynesia', 'Latvia', 2.0],
 ['French Polynesia', 'Panama', 3.0],
 ['French Polynesia', 'Russia', 3.0],
 ['French Polynesia', 'United Kingdom', 6.0],
 ['French Polynesia', 'United States', 2.0],
 ['Gabon', 'British Virgin Islands', 2.0],
 ['Gabon', 'Panama', 1.0],
 ['Gabon', 'Russia', 2.0],
 ['Georgia', 'British Anguilla', 2.0],
 ['Georgia', 'British Virgin Islands', 8.0],
 ['Georgia', 'China', 1.0],
 ['Georgia', 'Cook Islands', 0.5],
 ['Georgia', 'Ireland', 2.0],
 ['Georgia', 'Panama', 1.0],
 ['Georgia', 'Russia', 1.0],
 ['Georgia', 'United Arab Emirates', 1.0],
 ['Georgia', 'United Kingdom', 2.0],
 ['Germany', 'Bahamas', 2.0],
 ['Germany', 'Barbados', 6.0],
 ['Germany', 'Belize', 4.0],
 ['Germany', 'Bermuda', 8.0],
 ['Germany', 'British Anguilla', 2.0],
 ['Germany', 'British Virgin Islands', 222.5],
 ['Germany', 'Canada', 8.0],
 ['Germany', 'Cayman Islands', 10.0],
 ['Germany', 'China', 8.0],
 ['Germany', 'Cyprus', 19.0],
 ['Germany', 'Hong Kong', 53.0],
 ['Germany', 'Ireland', 11.0],
 ['Germany', 'Isle of Man', 2.0],
 ['Germany', 'Jersey', 3.0],
 ['Germany', 'Latvia', 7.0],
 ['Germany', 'Luxembourg', 2.0],
 ['Germany', 'Mauritius', 1.0],
 ['Germany', 'Niue', 4.0],
 ['Germany', 'Panama', 273.0],
 ['Germany', 'Russia', 31.0],
 ['Germany', 'Saint Kitts and Nevis', 7.0],
 ['Germany', 'Samoa', 1.5],
 ['Germany', 'Seychelles', 23.0],
 ['Germany', 'United Arab Emirates', 5.0],
 ['Germany', 'United Kingdom', 37.0],
 ['Germany', 'United States', 20.0],
 ['Ghana', 'Bahamas', 2.0],
 ['Ghana', 'British Anguilla', 2.0],
 ['Ghana', 'British Virgin Islands', 5.0],
 ['Ghana', 'Hong Kong', 1.0],
 ['Ghana', 'Russia', 2.0],
 ['Ghana', 'United Kingdom', 1.0],
 ['Ghana', 'United States', 1.0],
 ['Gibraltar', 'Bahamas', 300.0],
 ['Gibraltar', 'Barbados', 16.0],
 ['Gibraltar', 'Belize', 4.0],
 ['Gibraltar', 'British Virgin Islands', 3211.5],
 ['Gibraltar', 'Canada', 3.0],
 ['Gibraltar', 'Cayman Islands', 2.0],
 ['Gibraltar', 'China', 5.0],
 ['Gibraltar', 'Hong Kong', 12.0],
 ['Gibraltar', 'Ireland', 4.0],
 ['Gibraltar', 'Isle of Man', 8.0],
 ['Gibraltar', 'Jersey', 1.0],
 ['Gibraltar', 'Luxembourg', 1.0],
 ['Gibraltar', 'Malta', 2.0],
 ['Gibraltar', 'Nevada', 4.0],
 ['Gibraltar', 'Niue', 56.0],
 ['Gibraltar', 'Panama', 396.0],
 ['Gibraltar', 'Russia', 8.0],
 ['Gibraltar', 'Samoa', 28.0],
 ['Gibraltar', 'Seychelles', 128.0],
 ['Gibraltar', 'Singapore', 1.5],
 ['Gibraltar', 'Switzerland', 1.0],
 ['Gibraltar', 'Thailand', 1.0],
 ['Gibraltar', 'Ukraine', 1.0],
 ['Gibraltar', 'United Kingdom', 10.0],
 ['Gibraltar', 'United States', 1.0],
 ['Greece', 'Bahamas', 14.0],
 ['Greece', 'Bermuda', 10.0],
 ['Greece', 'British Virgin Islands', 172.0],
 ['Greece', 'Canada', 4.0],
 ['Greece', 'Cayman Islands', 2.0],
 ['Greece', 'China', 7.0],
 ['Greece', 'Cyprus', 3.0],
 ['Greece', 'Hong Kong', 10.0],
 ['Greece', 'Ireland', 2.0],
 ['Greece', 'Jersey', 1.0],
 ['Greece', 'Liberia', 2.0],
 ['Greece', 'Malta', 1.0],
 ['Greece', 'Panama', 273.0],
 ['Greece', 'Russia', 13.0],
 ['Greece', 'Seychelles', 43.0],
 ['Greece', 'United Arab Emirates', 1.0],
 ['Greece', 'United Kingdom', 12.0],
 ['Greece', 'United States', 1.0],
 ['Grenada', 'Barbados', 4.0],
 ['Grenada', 'British Virgin Islands', 4.0],
 ['Grenada', 'Panama', 1.0],
 ['Grenada', 'Saint Kitts and Nevis', 8.0],
 ['Guam', 'British Virgin Islands', 2.0],
 ['Guam', 'United States', 1.0],
 ['Guatemala', 'Bahamas', 78.0],
 ['Guatemala', 'British Anguilla', 46.0],
 ['Guatemala', 'British Virgin Islands', 370.0],
 ['Guatemala', 'Cayman Islands', 16.0],
 ['Guatemala', 'Niue', 280.0],
 ['Guatemala', 'Panama', 1602.0],
 ['Guatemala', 'Saint Kitts and Nevis', 8.0],
 ['Guatemala', 'Samoa', 32.0],
 ['Guatemala', 'Seychelles', 60.0],
 ['Guernsey', 'Bahamas', 3008.0],
 ['Guernsey', 'Barbados', 26.0],
 ['Guernsey', 'Bermuda', 6.0],
 ['Guernsey', 'British Anguilla', 54.0],
 ['Guernsey', 'British Virgin Islands', 9562.0],
 ['Guernsey', 'Canada', 5.0],
 ['Guernsey', 'Cayman Islands', 8.0],
 ['Guernsey', 'China', 9.0],
 ['Guernsey', 'Cook Islands', 4.0],
 ['Guernsey', 'Cyprus', 8.0],
 ['Guernsey', 'Czech Republic', 2.0],
 ['Guernsey', 'Hong Kong', 25.0],
 ['Guernsey', 'Ireland', 4.0],
 ['Guernsey', 'Isle of Man', 44.0],
 ['Guernsey', 'Jersey', 16.0],
 ['Guernsey', 'Luxembourg', 1.0],
 ['Guernsey', 'Niue', 604.0],
 ['Guernsey', 'Panama', 1423.0],
 ['Guernsey', 'Russia', 24.0],
 ...]

d = collections.defaultdict(int)
for n1, n2, v in df_weighted_grouped_list:
    d[min(n1, n2), max(n1, n2)] += v
result = [[k[0], k[1], v] for k, v in d.items()]

df_net = pd.DataFrame(result)
df_net

	0	1	2
0	Albania	Bahamas	4.0
1	Algeria	British Virgin Islands	2.0
2	Algeria	United Kingdom	2.0
3	Algeria	United States	1.0
4	Andorra	Bahamas	52.0
...	...	...	...
2383	Hong Kong	Zimbabwe	3.0
2384	Jersey	Zimbabwe	1.0
2385	Panama	Zimbabwe	6.0
2386	Russia	Zimbabwe	2.0
2387	United States	Zimbabwe	1.0

2388 rows × 3 columns

df_net = df_net.rename(columns = {0:'node_1', 1:'node_2', 2: 'weight'})
df_net

	node_1	node_2	weight
0	Albania	Bahamas	4.0
1	Algeria	British Virgin Islands	2.0
2	Algeria	United Kingdom	2.0
3	Algeria	United States	1.0
4	Andorra	Bahamas	52.0
...	...	...	...
2383	Hong Kong	Zimbabwe	3.0
2384	Jersey	Zimbabwe	1.0
2385	Panama	Zimbabwe	6.0
2386	Russia	Zimbabwe	2.0
2387	United States	Zimbabwe	1.0

2388 rows × 3 columns

#df_net.to_csv('undirected_weighted_grouped.csv', index = False)

df_net.describe()

	weight
count	2388.000000
mean	231.778266
std	1907.448852
min	0.500000
25%	2.000000
50%	5.000000
75%	30.000000
max	66262.500000

Create Graph

g = ig.Graph.TupleList(df_net.itertuples(index=False), directed=False, edge_attrs={'weight': "weight"})

ig.plot(g)

g.diameter()

3

print( "Clustering Coefficient: ", g.transitivity_undirected())

Clustering Coefficient:  0.3859593515919117

np.mean(g.degree())

24.367346938775512

np.mean(g.strength(weights = 'weight'))

5647.821428571428

Metrics

def get_diameter(community):
    
    diameters = []
    
    for i in range(len(community.subgraphs())):
    
        diameters.append(community.subgraphs()[i].diameter())

    return (diameters)

def get_transitivity(community):
    
    transitivity = []
    
    for i in range(len(community.subgraphs())):
    
        transitivity.append(community.subgraphs()[i].transitivity_undirected())

    return (transitivity)

def get_avg_degree(community):
    
    degree = []
    
    for i in range(len(community.subgraphs())):
    
        degree.append(np.mean(community.subgraphs()[i].degree()))

    return degree

def get_avg_weighted_degree(community):
    
    weighted_degree = []
    
    for i in range(len(community.subgraphs())):
    
        weighted_degree.append(np.mean(community.subgraphs()[i].strength(weights = 'weight')))

    return weighted_degree

Community Detection

Louvain Community Detection

comm_louvain = g.community_multilevel(weights = 'weight')

ig.plot(comm_louvain, mark_groups = True, layout="drl")

modularity = g.modularity(comm_louvain, weights='weight')
print(modularity)

0.161520817356064

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_louvain))
print('average degree: ', get_avg_degree(comm_louvain))
print('transitivity: ', get_transitivity(comm_louvain))
print('diameter: ', get_diameter(comm_louvain))

average weighted degree:  [3196.782608695652, 5119.582417582417, 438.3529411764706, 845.0]
average degree:  [8.26086956521739, 14.285714285714286, 5.764705882352941, 1.0]
transitivity:  [0.2930897537728356, 0.3796081923419412, 0.3496932515337423, nan]
diameter:  [4, 2, 3, 1]

df_louvain = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_louvain.membership } )
df_louvain

	name	community
0	Albania	0
1	Bahamas	0
2	Algeria	1
3	British Virgin Islands	1
4	United Kingdom	1
...	...	...
191	Venezuela	0
192	Vietnam	1
193	Yemen	0
194	Zambia	1
195	Zimbabwe	1

196 rows × 2 columns

df_louvain.describe()

	community
count	196.000000
mean	0.841837
std	0.737622
min	0.000000
25%	0.000000
50%	1.000000
75%	1.000000
max	3.000000

df_louvain[df_louvain['community'] == 0]

	name	community
0	Albania	0
1	Bahamas	0
6	Andorra	0
9	Costa Rica	0
13	Nevada	0
...	...	...
178	Sudan	0
180	Syria	0
189	Dubai	0
191	Venezuela	0
193	Yemen	0

69 rows × 2 columns

df_louvain[df_louvain['community'] == 1].head(99)

	name	community
2	Algeria	1
3	British Virgin Islands	1
4	United Kingdom	1
8	Cook Islands	1
10	Hong Kong	1
...	...	...
188	Uganda	1
190	Uzbekistan	1
192	Vietnam	1
194	Zambia	1
195	Zimbabwe	1

91 rows × 2 columns

df_louvain[df_louvain['community'] == 2]

	name	community
5	United States	2
7	Cayman Islands	2
17	Samoa	2
21	Bermuda	2
24	Barbados	2
26	Saint Kitts and Nevis	2
29	New Zealand	2
35	Austria	2
45	Netherlands	2
48	Sweden	2
54	Netherlands Antilles	2
55	Saint Lucia	2
64	Turks and Caicos Islands	2
72	Curaçao	2
74	Marshall Islands	2
75	State of Delaware	2
82	Grenada	2
84	Saint Vincent and the Grenadines	2
89	France	2
95	Cuba	2
97	Denmark	2
107	French Guiana	2
112	Guam	2
115	Guyana	2
128	Jamaica	2
154	Nigeria	2
155	Norfolk Island	2
157	Norway	2
166	Puerto Rico	2
169	Saint Martin (French part)	2
170	Saudi Arabia	2
172	Sierra Leone	2
179	Suriname	2
184	Trinidad and Tobago	2

df_louvain[df_louvain['community'] == 3]

	name	community
42	Labuan	3
44	Malaysia	3

df_louvain[df_louvain['community'] == 4]

	name	community

# Define colors used for visualization
colors = ['blue', 'yellow', 'green','purple', 'pink', 'orange', 'lightcoral', 'plum', 'darkgreen', 'darkblue', 'rosybrown',
         'crimson', 'steelblue', 'khaki', 'moccasin', 'orangered', 'fuchsia', 'thistle', 'salmon', 'chocolate', 'lightseagreen',
         'springgreen', 'darkseagreen', 'deeppink', 'mediumvioletred','olive']

##plot community
visual_style = {}

vertex_colors = []

for i in range(len(comm_louvain.membership)):
    vertex_colors.append(colors[comm_louvain.membership[i]])
    
# Set bbox and margin
visual_style["bbox"] = (5000,5000)
visual_style["margin"] = 17

# Set vertex colours
visual_style["vertex_color"] = vertex_colors

# Set edge width
visual_style["edge_width"] = np.log(g.es['weight']) + 1

# Set edge color
visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# Set vertex size
visual_style["vertex_size"] = 15

# Set vertex label size
visual_style["vertex_label_size"] = 20

# Set vertex label color
visual_style["vertex_label_color"] = 'red'

# Don't curve the edges
visual_style["edge_curved"] = False

# Set the layout
my_layout = g.layout_fruchterman_reingold()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'images/louvain_communities.png', vertex_label = g.vs['name'] , **visual_style)

# Set the layout
my_layout = g.layout_circle()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'images/louvain_circular.png', vertex_label = g.vs['name'] , **visual_style)

for i in range(len(comm_louvain.subgraphs())):
    
    visual_style["bbox"] = (2000,2000)
    visual_style["margin"] = 17
    
    # Set vertex colours
    visual_style["vertex_color"] = 'green'

    # Set edge width
    visual_style["edge_width"] = np.log(comm_louvain.subgraphs()[i].es['weight']) + 1
    # Set the layout
    my_layout = comm_louvain.subgraphs()[i].layout_fruchterman_reingold()
    visual_style["layout"] = my_layout

    ig.plot(comm_louvain.subgraphs()[i],'images/louvain_subgraph' + str(i) + '.png' ,vertex_label = comm_louvain.subgraphs()[i].vs['name'],**visual_style  )
    
    
    

# # Set vertex colours

# visual_style["bbox"] = (300,300)
# visual_style["margin"] = 17
# visual_style["vertex_color"] = 'orange'

# # Set vertex label size
# visual_style["vertex_label_size"] = 15

# # Set the layout
# my_layout = comm_louvain.cluster_graph().layout_fruchterman_reingold()
# visual_style["layout"] = my_layout

# ig.plot(comm_louvain.cluster_graph(),'images/test_cluster_graph.png' ,vertex_label = range(comm_louvain.cluster_graph().vcount()),**visual_style  )

Label Propogation Community Detection

comm_lp= g.community_label_propagation(weights = 'weight')

ig.plot(comm_lp, mark_groups = True, layout="drl")

modularity = g.modularity(comm_lp, weights='weight')
print(modularity)

0.0

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_lp))
print('average degree: ', get_avg_degree(comm_lp))
print('transitivity: ', get_transitivity(comm_lp))
print('diameter: ', get_diameter(comm_lp))

average weighted degree:  [5647.821428571428]
average degree:  [24.367346938775512]
transitivity:  [0.3859593515919117]
diameter:  [3]

Leading Eigenvector Community Detection

comm_leading_eigenvector= g.community_leading_eigenvector(weights = 'weight')

ig.plot(comm_leading_eigenvector, mark_groups = True, layout="drl")

modularity = g.modularity(comm_leading_eigenvector, weights='weight')
print(modularity)

0.15025680457793572

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_leading_eigenvector))
print('average degree: ', get_avg_degree(comm_leading_eigenvector))
print('transitivity: ', get_transitivity(comm_leading_eigenvector))
print('diameter: ', get_diameter(comm_leading_eigenvector))

average weighted degree:  [3447.0862068965516, 4977.735042735043, 0.85, 0.0]
average degree:  [7.620689655172414, 19.435897435897434, 0.2, 0.0]
transitivity:  [0.2783573806881243, 0.45566413318856314, nan, nan]
diameter:  [3, 2, 1, 0]

df_leading_eigenvector = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_leading_eigenvector.membership } )
df_leading_eigenvector

	name	community
0	Albania	0
1	Bahamas	0
2	Algeria	1
3	British Virgin Islands	1
4	United Kingdom	1
...	...	...
191	Venezuela	0
192	Vietnam	1
193	Yemen	0
194	Zambia	1
195	Zimbabwe	1

196 rows × 2 columns

df_leading_eigenvector.describe()

	community
count	196.000000
mean	0.816327
std	0.621775
min	0.000000
25%	0.000000
50%	1.000000
75%	1.000000
max	3.000000

##plot community
visual_style = {}

vertex_colors = []

for i in range(len(comm_leading_eigenvector.membership)):
    vertex_colors.append(colors[comm_leading_eigenvector.membership[i]])
    
# Set bbox and margin
visual_style["bbox"] = (5000,5000)
visual_style["margin"] = 17

# Set vertex colours
visual_style["vertex_color"] = vertex_colors

# Set edge width
visual_style["edge_width"] = np.log(g.es['weight']) + 1

# Set edge color
visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# Set vertex size
visual_style["vertex_size"] = 15

# Set vertex label size
visual_style["vertex_label_size"] = 20

# Set vertex label color
visual_style["vertex_label_color"] = 'red'

# Don't curve the edges
visual_style["edge_curved"] = False

# Set the layout
my_layout = g.layout_fruchterman_reingold()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'images/communities_leading_eigenvector.png', vertex_label = g.vs['name'] , **visual_style)

# Set the layout
my_layout = g.layout_circle()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'images/leading_vector_circular.png', vertex_label = g.vs['name'] , **visual_style)

for i in range(len(comm_leading_eigenvector.subgraphs())):
    
    visual_style["bbox"] = (2000,2000)
    visual_style["margin"] = 17
    
    # Set vertex colours
    visual_style["vertex_color"] = 'green'

    # Set edge width
    visual_style["edge_width"] = np.log(comm_leading_eigenvector.subgraphs()[i].es['weight']) + 1
    # Set the layout
    my_layout = comm_leading_eigenvector.subgraphs()[i].layout_fruchterman_reingold()
    visual_style["layout"] = my_layout

    ig.plot(comm_leading_eigenvector.subgraphs()[i],'images/leading_eigenvector_subgraph' + str(i) + '.png' ,vertex_label = comm_leading_eigenvector.subgraphs()[i].vs['name'],**visual_style  )
    

Leiden Community Detection

comm_leiden = g.community_leiden(weights = 'weight')

ig.plot(comm_leiden, mark_groups = True, layout="drl")

modularity = g.modularity(comm_leiden, weights='weight')
print(modularity)

-5.6863773329566016e-06

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_leiden))
print('average degree: ', get_avg_degree(comm_leiden))
print('transitivity: ', get_transitivity(comm_leiden))
print('diameter: ', get_diameter(comm_leiden))

average weighted degree:  [0.0, 10908.386138613861, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
average degree:  [0.0, 36.31683168316832, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
transitivity:  [nan, 0.5556558269799284, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
diameter:  [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

df_leiden = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_leiden.membership } )
df_leiden

	name	community
0	Albania	0
1	Bahamas	1
2	Algeria	2
3	British Virgin Islands	1
4	United Kingdom	1
...	...	...
191	Venezuela	1
192	Vietnam	1
193	Yemen	93
194	Zambia	94
195	Zimbabwe	95

196 rows × 2 columns

df_leiden.describe()

	community
count	196.000000
mean	23.775510
std	30.350611
min	0.000000
25%	1.000000
50%	1.000000
75%	46.250000
max	95.000000

## no visualisation as most communities are individual?

Optimal Modularity Community Detection

# #SLOW

# comm_opt_modularity = g.community_optimal_modularity(weights = 'weight')

# ig.plot(comm_opt_modularity, mark_groups = True, layout="drl")

# modularity = g.modularity(comm_opt_modularity, weights='weight')
# print(modularity)

# df_opt_modularity = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_opt_modularity.membership } )
# df_opt_modularity

# df_opt_modularity.describe

# ##plot community
# visual_style = {}

# vertex_colors = []

# for i in range(len(df_opt_modularity.membership)):
#     vertex_colors.append(colors[df_opt_modularity.membership[i]])
    
# # Set bbox and margin
# visual_style["bbox"] = (5000,5000)
# visual_style["margin"] = 17

# # Set vertex colours
# visual_style["vertex_color"] = vertex_colors

# # Set edge width
# visual_style["edge_width"] = np.log(g.es['weight']) + 1

# # Set edge color
# visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# # Set vertex size
# visual_style["vertex_size"] = 15

# # Set vertex label size
# visual_style["vertex_label_size"] = 20

# # Set vertex label color
# visual_style["vertex_label_color"] = 'red'

# # Don't curve the edges
# visual_style["edge_curved"] = False

# # Set the layout
# my_layout = g.layout_fruchterman_reingold()
# visual_style["layout"] = my_layout

# # Plot the graph
# ig.plot(g,'images/communities_optimal_modularity.png', vertex_label = g.vs['name'] , **visual_style)

# # Set the layout
# my_layout = g.layout_circle()
# visual_style["layout"] = my_layout

# # Plot the graph
# ig.plot(g,'images/optimal_modularity_circular.png', vertex_label = g.vs['name'] , **visual_style)

# for i in range(len(optimal_modularity.subgraphs())):
    
#     visual_style["bbox"] = (2000,2000)
#     visual_style["margin"] = 17
    
#     # Set vertex colours
#     visual_style["vertex_color"] = 'green'

#     # Set edge width
#     visual_style["edge_width"] = np.log(optimal_modularity.subgraphs()[i].es['weight']) + 1
#     # Set the layout
#     my_layout = optimal_modularity_circular.subgraphs()[i].layout_fruchterman_reingold()
#     visual_style["layout"] = my_layout

#     ig.plot(optimal_modularity_circular.subgraphs()[i],'images/optimal_modularity_subgraph' + str(i) + '.png' ,vertex_label = optimal_modularity.subgraphs()[i].vs['name'],**visual_style  )
    

Spinglass Community Detection

comm_spinglass = g.community_spinglass(weights = 'weight')

ig.plot(comm_spinglass, mark_groups = True, layout="drl")

modularity = g.modularity(comm_spinglass, weights='weight')
print(modularity)

0.16231230985453723

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_spinglass))
print('average degree: ', get_avg_degree(comm_spinglass))
print('transitivity: ', get_transitivity(comm_spinglass))
print('diameter: ', get_diameter(comm_spinglass))

average weighted degree:  [0.0, 230.0, 0.0, 5437.364705882353, 3557.0508474576272, 845.0, 415.85, 264.0]
average degree:  [0.0, 1.0, 0.0, 12.894117647058824, 7.694915254237288, 1.0, 6.6, 2.0]
transitivity:  [nan, nan, nan, 0.3565983398272065, 0.2964659685863874, nan, 0.38422818791946306, 0.0]
diameter:  [0, 1, 0, 2, 4, 1, 3, 2]

df_spinglass = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_spinglass.membership } )
df_spinglass

	name	community
0	Albania	4
1	Bahamas	4
2	Algeria	3
3	British Virgin Islands	3
4	United Kingdom	3
...	...	...
191	Venezuela	4
192	Vietnam	3
193	Yemen	4
194	Zambia	3
195	Zimbabwe	3

196 rows × 2 columns

df_spinglass.describe()

	community
count	196.000000
mean	4.015306
std	1.318418
min	0.000000
25%	3.000000
50%	4.000000
75%	4.000000
max	7.000000

##plot community
visual_style = {}

vertex_colors = []

for i in range(len(comm_spinglass.membership)):
    vertex_colors.append(colors[comm_spinglass.membership[i]])
    
# Set bbox and margin
visual_style["bbox"] = (5000,5000)
visual_style["margin"] = 17

# Set vertex colours
visual_style["vertex_color"] = vertex_colors

# Set edge width
visual_style["edge_width"] = np.log(g.es['weight']) + 1

# Set edge color
visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# Set vertex size
visual_style["vertex_size"] = 15

# Set vertex label size
visual_style["vertex_label_size"] = 20

# Set vertex label color
visual_style["vertex_label_color"] = 'red'

# Don't curve the edges
visual_style["edge_curved"] = False

# Set the layout
my_layout = g.layout_fruchterman_reingold()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'images/communities_spinglass.png', vertex_label = g.vs['name'] , **visual_style)

# Set the layout
my_layout = g.layout_circle()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'images/spinglass_circular.png', vertex_label = g.vs['name'] , **visual_style)

for i in range(len(comm_spinglass.subgraphs())):
    
    visual_style["bbox"] = (2000,2000)
    visual_style["margin"] = 17
    
    # Set vertex colours
    visual_style["vertex_color"] = 'green'

    # Set edge width
    visual_style["edge_width"] = np.log(comm_spinglass.subgraphs()[i].es['weight']) + 1
    # Set the layout
    my_layout = comm_spinglass.subgraphs()[i].layout_fruchterman_reingold()
    visual_style["layout"] = my_layout

    ig.plot(comm_spinglass.subgraphs()[i],'images/spinglass_subgraph' + str(i) + '.png' ,vertex_label = comm_spinglass.subgraphs()[i].vs['name'],**visual_style  )
    

Walktrap Community Detection

walktrap = g.community_walktrap(weights = 'weight', steps = 2)

comm_walktrap = walktrap.as_clustering()

ig.plot(comm_walktrap, mark_groups = True, layout="drl")

modularity = g.modularity(comm_walktrap, weights='weight')
print(modularity)

0.0020223572959788337

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_walktrap))
print('average degree: ', get_avg_degree(comm_walktrap))
print('transitivity: ', get_transitivity(comm_walktrap))
print('diameter: ', get_diameter(comm_walktrap))

average weighted degree:  [0.0, 7984.430656934306, 0.0, 0.0, 143.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
average degree:  [0.0, 30.56934306569343, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
transitivity:  [nan, 0.44463040899562895, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
diameter:  [0, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

df_walktrap = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_walktrap.membership } )
df_walktrap

	name	community
0	Albania	0
1	Bahamas	1
2	Algeria	2
3	British Virgin Islands	1
4	United Kingdom	1
...	...	...
191	Venezuela	1
192	Vietnam	1
193	Yemen	51
194	Zambia	52
195	Zimbabwe	1

196 rows × 2 columns

df_walktrap.describe()

	community
count	196.000000
mean	7.867347
std	13.652566
min	0.000000
25%	1.000000
50%	1.000000
75%	4.000000
max	52.000000

## no visualisation since too many communities, mostly individual

Infomap Community Detection

#get metrics for each community

comm_infomap = g.community_infomap(edge_weights = 'weight')

ig.plot(comm_infomap, mark_groups = True, layout="drl")

modularity = g.modularity(comm_infomap, weights='weight')
print(modularity)

0.0

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_infomap))
print('average degree: ', get_avg_degree(comm_infomap))
print('transitivity: ', get_transitivity(comm_infomap))
print('diameter: ', get_diameter(comm_infomap))

average weighted degree:  [5647.821428571428]
average degree:  [24.367346938775512]
transitivity:  [0.3859593515919117]
diameter:  [3]

df_infomap = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_infomap.membership } )
df_infomap

	name	community
0	Albania	0
1	Bahamas	0
2	Algeria	0
3	British Virgin Islands	0
4	United Kingdom	0
...	...	...
191	Venezuela	0
192	Vietnam	0
193	Yemen	0
194	Zambia	0
195	Zimbabwe	0

196 rows × 2 columns

## no visualisation since no community is detected

Fast Greedy Community Detection

fg = g.community_fastgreedy(weights = 'weight')

comm_fastgreedy = fg.as_clustering()

ig.plot(comm_fastgreedy, mark_groups = True, layout="drl")

modularity = g.modularity(comm_fastgreedy, weights='weight')
print(modularity)

0.16231365315017704

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_fastgreedy))
print('average degree: ', get_avg_degree(comm_fastgreedy))
print('transitivity: ', get_transitivity(comm_fastgreedy))
print('diameter: ', get_diameter(comm_fastgreedy))

average weighted degree:  [3497.8, 5374.162790697675, 415.85, 264.0, 845.0, 230.0]
average degree:  [7.6, 12.767441860465116, 6.6, 2.0, 1.0, 1.0]
transitivity:  [0.295627583206439, 0.35491485415612883, 0.38422818791946306, 0.0, nan, nan]
diameter:  [4, 2, 3, 2, 1, 1]

df_fastgreedy = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_fastgreedy.membership } )
df_fastgreedy

	name	community
0	Albania	0
1	Bahamas	0
2	Algeria	1
3	British Virgin Islands	1
4	United Kingdom	1
...	...	...
191	Venezuela	0
192	Vietnam	1
193	Yemen	0
194	Zambia	1
195	Zimbabwe	1

196 rows × 2 columns

df_fastgreedy.describe()

	community
count	196.000000
mean	1.030612
std	0.944122
min	0.000000
25%	0.000000
50%	1.000000
75%	2.000000
max	5.000000

##plot community
visual_style = {}

vertex_colors = []

for i in range(len(comm_fastgreedy.membership)):
    vertex_colors.append(colors[comm_fastgreedy.membership[i]])
    
# Set bbox and margin
visual_style["bbox"] = (5000,5000)
visual_style["margin"] = 17

# Set vertex colours
visual_style["vertex_color"] = vertex_colors

# Set edge width
visual_style["edge_width"] = np.log(g.es['weight']) + 1

# Set edge color
visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# Set vertex size
visual_style["vertex_size"] = 15

# Set vertex label size
visual_style["vertex_label_size"] = 20

# Set vertex label color
visual_style["vertex_label_color"] = 'red'

# Don't curve the edges
visual_style["edge_curved"] = False

# Set the layout
my_layout = g.layout_fruchterman_reingold()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'images/communities_fastgreedy.png', vertex_label = g.vs['name'] , **visual_style)

# Set the layout
my_layout = g.layout_circle()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'images/fastgreedy_circular.png', vertex_label = g.vs['name'] , **visual_style)

for i in range(len(comm_fastgreedy.subgraphs())):
    
    visual_style["bbox"] = (2000,2000)
    visual_style["margin"] = 17
    
    # Set vertex colours
    visual_style["vertex_color"] = 'green'

    # Set edge width
    visual_style["edge_width"] = np.log(comm_fastgreedy.subgraphs()[i].es['weight']) + 1
    # Set the layout
    my_layout = comm_fastgreedy.subgraphs()[i].layout_fruchterman_reingold()
    visual_style["layout"] = my_layout

    ig.plot(comm_fastgreedy.subgraphs()[i],'images/fastgreedy_subgraph' + str(i) + '.png' ,vertex_label = comm_fastgreedy.subgraphs()[i].vs['name'],**visual_style  )